To alleviate the aforementioned issues, a group of works explores learning based approaches to directly align and reconstruct pixels in all saturated regions. Some works proposed a pre-processing step to align the multi-exposure images prior to feeding them to a CNN. For example, pre-processed steps based on optical flow and homography transformation are proposed, respectively by Kalantari et al. [1]} and Wu et al. [2]}. Other works implemented an end-to-end HDR framework. For example, Yan et al. [3]}, Liu et al. [4]} and Pu et al. [5]} presented attention-merging networks to generate ghost-free HDR imaging. Ma et al. [6]} proposed an attention-merging network with guided image filtering to optimize the fusion weight maps of the multi-exposure images. Niu et al. [7]} implemented a generative adversarial network (GAN) based model to fuse the multi-exposure images into an HDR image. In a similar vein, Kalantari et al. [8]} uses two connected networks to fuse multiple LDR frames, and generate an HDR video. All of these methods need multiple shots, though some methods have accounted for motions between the input images [3]}, [4]}, [5]}, suppressing severe motions remain a challenge thus is not practical in applications with moving cameras. On the other hand, perfectly aligned polarization images can be captured in just a single-shot, which is suitable for applications with both stationary and moving cameras.
